[whatmodel.sh](../../README.md) / Grok 4 Fast # Grok 4 Fast SpaceXAI (xAI) · released 2025-09 · closed weights **score 60.6** ±1.4 · comparison only: no current run option verified · from 9 benchmark results across 5 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Grok 4 Fast among every model on that task or test, worst to best.}

task                vs the others          rank  counts  tests
agent tasks         ▁▁▂▆█▄●▄▄▃▁▁   #136 of 354*    6.6%      1
coding              ▁▁▁▃▄▆●▇▅▄▃▁   #259 of 539*     36%      1
chat & writing      ▁▁▁▃▅▄▇█▇●▇▃   #127 of 474*     21%      1
vision & documents  ▁▁▂●▂▃▅▄▆██▄   #161 of 189*     12%      2
web search          ▂▁▂▆●█▅▄▁▂▁▁     #64 of 96*    7.4%      1

agent tasks · #136 of 354 · 6.6% of its score

Beats [o3-pro](o3-pro.md), [o3](o3.md), [GPT-5 mini](gpt-5-mini.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Large](jamba-1-5-large.md) here, though they rank lower overall.

test                                result  vs the others in its run  difficulty            counts  why
τ³-bench banking_knowledge (v1.0.…   15.7%  ▃▃●▃·▆▃▂█▅▅▂ beats 4/25   very hard · avg 4.3%    6.6%  typical

coding · #259 of 539 · 36% of its score

Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md), [s1.1 32B](s1-1-32b.md) here, though they rank lower overall.

test                       result  vs the others in its run  difficulty  counts  why
Arena WebDev (Code Arena)  1159.1  ▁●▃▂▅█▃▆▅▅▁▂ beats 1/129  avg 981        36%  sharp

chat & writing · #127 of 474 · 21% of its score

Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [GPT-5.4 nano](gpt-5-4-nano.md), [Step 3.5 Flash (196B-A11B)](step-3-5-flash.md) here, though they rank higher overall.

test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1405.3  ▁▁▁▂▃▃▃▆▆●█▃ beats 250/385  avg 1260       21%  blunt

vision & documents · #161 of 189 · 12% of its score

Trails [Qwen3.5 2B](qwen3-5-2b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md), [Qwen2.5-VL 7B Instruct](qwen2-5-vl-7b.md) here, though they rank lower overall.

test                           result  vs the others in its run  difficulty      counts  why
CharXiv (reasoning questions)   33.5%  ●·····▄▄·▄·█ beats 0/5    hard · avg 31%    7.6%  at its level
CharXiv descriptive questions   68.2%  ●····▄···█▄▄ beats 0/5    easy · avg 67%    4.7%  noisy

web search · #64 of 96 · 7.4% of its score

Beats [Kimi K2.6](kimi-k2-6.md), [GPT-5 Pro](gpt-5-pro.md), [DeepSeek V4 Flash (Preview, Apr 2026)](deepseek-v4-flash-preview.md) here, though they rank higher overall. Trails [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank lower overall.

test                                result  vs the others in its run  difficulty  counts  why
Arena search systems (style contr…  1161.1  ▁·▁▁▂●▂█▆▃▂▂ beats 6/36   avg 1069      7.4%  blunt

other tests (3)

test                                result  vs the others in its run  difficulty            counts  why
ARC-AGI-1 public evaluation set (…   58.1%  ▁·▇▃▁·●▇▁·▇▃ beats 10/27  very hard · avg 4.0%     13%  sharp · at its level · noisy
ARC-AGI-2 public evaluation set (…    4.6%  █··▂●▁▃▁▃·▁▃ beats 9/24   very hard · avg 0.3%    3.3%  sharp · too hard for it · noisy
Vectara hallucination leaderboard…   79.8%  ▁●▁▂▅█▂▃▄▃▁▁ beats 4/98   very easy · avg 90%     0.0%  blunt

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
LMArena leaderboard dataset                     3   64% ▇▇▇▇▇▇▇▇
ARC Prize Foundation model_baseline out…        2   16% ▇▇
ServiceNow-AI/Apriel-1.6-15b-Thinker mo…        2   12% ▇
τ²/τ³-bench leaderboard: Sierra's own e…        1    7% ▇
Vectara hallucination leaderboard               1    0% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 60.6

_{Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · ARC Prize Foundation model\_baseline outputs on ARC-AGI-1/2 public evaluation sets ([MIT](../../licenses/arcprize-model-baseline-outputs-MIT.txt)) · ServiceNow-AI/Apriel-1.6-15b-Thinker model card ([MIT](../../licenses/cards-final-NOTICE.txt)) · τ²/τ³-bench leaderboard: Sierra's own evaluation runs ([MIT](https://opensource.org/license/mit)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))} ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/grok-4-fast) · [JSON](https://whatmodel.sh/api?format=json&all=1)